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Differential Privacy via Distributionally Robust Optimization. (arXiv:2304.12681v1 [cs.CR])
cs.CR updates on arXiv.org arxiv.org
In recent years, differential privacy has emerged as the de facto standard
for sharing statistics of datasets while limiting the disclosure of private
information about the involved individuals. This is achieved by randomly
perturbing the statistics to be published, which in turn leads to a
privacy-accuracy trade-off: larger perturbations provide stronger privacy
guarantees, but they result in less accurate statistics that offer lower
utility to the recipients. Of particular interest are therefore optimal
mechanisms that provide the highest accuracy for …
accuracy area datasets differential privacy disclosure information interest offer optimization privacy private result sharing standard statistics trade turn utility work